메뉴 건너뛰기




Volumn 10, Issue 3, 1990, Pages 49-55

Use of Neural Networks for Sensor Failure Deteotion in a Control System

Author keywords

[No Author keywords available]

Indexed keywords

CONTROL SYSTEMS; MATHEMATICAL TECHNIQUES--ERROR ANALYSIS; SENSORS--FAILURE;

EID: 0025419936     PISSN: 02721708     EISSN: None     Source Type: Journal    
DOI: 10.1109/37.55124     Document Type: Article
Times cited : (119)

References (16)
  • 1
    • 0021468997 scopus 로고
    • Process Fault Detection Based on Modeling and Estimation Methods— A Survey
    • R. Isermann, “Process Fault Detection Based on Modeling and Estimation Methods— A Survey,” Automalica, vol. 20, pp. 387–404, 1984.
    • (1984) Automalica , vol.20 , pp. 387-404
    • Isermann, R.1
  • 2
    • 0017022844 scopus 로고
    • A Survey of Design Methods for Failure Detection in Dynamic Systems
    • A. S. Willsky, “A Survey of Design Methods for Failure Detection in Dynamic Systems,” Automatica, vol. 12, pp. 601–611, 1976.
    • (1976) Automatica , vol.12 , pp. 601-611
    • Willsky, A.S.1
  • 3
    • 0021597513 scopus 로고
    • Robust Fault Detection: The Effect of Model Error
    • San Diego, CA
    • R. L. Kosut and R. A. Walker, “Robust Fault Detection: The Effect of Model Error,” Proc. 1984 Amer. Contr. Conf., San Diego, CA, pp. 1094–1096, 1984.
    • (1984) Proc. 1984 Amer. Contr. Conf. , pp. 1094-1096
    • Kosut, R.L.1    Walker, R.A.2
  • 4
    • 0242682494 scopus 로고
    • An Integrated Approach to Controls and Diagnostics: The 4-Parameter Controller
    • Atlanta, GA
    • C. N. Nett, C. A. Jacobson, and A. T. Miller, “An Integrated Approach to Controls and Diagnostics: The 4-Parameter Controller,” Proc. 1988 Amer. Contr. Conf., Atlanta, GA, pp. 824–835, 1988.
    • (1988) Proc. 1988 Amer. Contr. Conf. , pp. 824-835
    • Nett, C.N.1    Jacobson, C.A.2    Miller, A.T.3
  • 5
    • 0024125566 scopus 로고
    • Effect of Model Uncertainty on Failure Detection: The Threshold Selector
    • A. Emami-Naeini, M. M. Akhter, and S. M. Rock, “Effect of Model Uncertainty on Failure Detection: The Threshold Selector,” IEEE Trans. Automat. Contr., vol. 33, pp. 1106–1115, 1988.
    • (1988) IEEE Trans. Automat. Contr. , vol.33 , pp. 1106-1115
    • Emami-Naeini, A.1    Akhter, M.M.2    Rock, S.M.3
  • 9
    • 0023843391 scopus 로고
    • Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets
    • R. P. Gorman and T. J. Sejnowski, “Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets,” Neural Networks, vol. 1, pp. 75–89, 1988.
    • (1988) Neural Networks , vol.1 , pp. 75-89
    • Gorman, R.P.1    Sejnowski, T.J.2
  • 11
    • 0040287998 scopus 로고
    • Artificial Neural Network Models of Knowledge Representation in Process Engineering
    • J. C. Hoskins and D. M. Himmelblau, “Artificial Neural Network Models of Knowledge Representation in Process Engineering,” Computers and Chemical Engineering, vol. 12, pp. 881–915, 1988.
    • (1988) Computers and Chemical Engineering , vol.12 , pp. 881-915
    • Hoskins, J.C.1    Himmelblau, D.M.2
  • 12
    • 0024647864 scopus 로고
    • Neural Computing for Numeric-to-Symbolic Conversion in Control Systems
    • K. M. Passino, M. A. Sartori, and P. J. Antsaklis, “Neural Computing for Numeric-to-Symbolic Conversion in Control Systems,” IEEE Contr. Syst. Mag., vol. 9, pp. 44–52, 1989.
    • (1989) IEEE Contr. Syst. Mag. , vol.9 , pp. 44-52
    • Passino, K.M.1    Sartori, M.A.2    Antsaklis, P.J.3
  • 13
    • 0024137490 scopus 로고
    • Increased Rates of Convergence Through Learning Rate Adaptation
    • R. A. Jacobs, “Increased Rates of Convergence Through Learning Rate Adaptation,” Neural Networks, vol. 1, pp. 295–307, 1988.
    • (1988) Neural Networks , vol.1 , pp. 295-307
    • Jacobs, R.A.1
  • 14
  • 15
    • 0023834037 scopus 로고
    • Applications of Counter Propagation Networks
    • R. Hecht-Nielsen, “Applications of Counter Propagation Networks,” Neural Networks, vol. 1, pp. 131–139, 1988.
    • (1988) Neural Networks , vol.1 , pp. 131-139
    • Hecht-Nielsen, R.1
  • 16
    • 0023846591 scopus 로고
    • Neocognitron: A Hierarchical Neural Network Capable of Visual Pattern Recognition
    • K. Fukushima, “Neocognitron: A Hierarchical Neural Network Capable of Visual Pattern Recognition,” Neural Networks, vol. 1, pp. 119–130, 1988.
    • (1988) Neural Networks , vol.1 , pp. 119-130
    • Fukushima, K.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.